Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct

Identification of L5 vertebra on lumbar spine radiographs using deep learning

OBJECTIVE

Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and assessed its detection accuracy.

METHODS

We retrospectively recruited 150 participants for whom both anteroposterior whole-spine and lumbar spine radiographs were available. The anteroposterior lumbar spine radiographs of these patients were used as the input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x, of the YOLOv5 family model, was used to detect the L5 vertebra area.

RESULTS

The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model's precision was 95.7% and its recall was 97.8%. Furthermore, 93.3% of the validation data were correctly detected.

CONCLUSION

Our deep learning model showed an outstanding ability to identify L5 vertebrae.

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recovery of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recovery of language, cognitive, and sensory function, as well as prescribing appropriate rehabilitation treatments.

Convolutional Neural Network Algorithm Trained with Anteroposterior Radiographs to Diagnose Pre-Collapse Osteonecrosis of the Femoral Head

A convolutional neural network (CNN) is a representative deep-learning algorithm that has a significant advantage in image recognition and classification. Using anteroposterior pelvic radiographs as input data, we developed a CNN algorithm to determine the presence of pre-collapse osteonecrosis of the femoral head (ONFH). We developed a CNN algorithm to differentiate between ONFH and normal radiographs. We retrospectively included 305 anteroposterior pelvic radiographs (right hip: pre-collapsed ONFH = 79, normal = 226; left hip: pre-collapsed ONFH = 62, normal = 243) as data samples. Pre-collapsed ONFH was diagnosed using pelvic magnetic resonance imaging data for each patient. Among the 305 cases, 69.8% of the included data samples were randomly selected as the training set, 21.0% were selected as the validation set, and the remaining 9.2% were selected as the test set to evaluate the performance of the developed CNN algorithm. The area under the curve of our developed CNN algorithm on the test data was 0.912 (95% confidence interval, 0.773–1.000) for the right hip and 0.902 (95% confidence interval, 0.747–1.000) for the left hip. We showed that a CNN algorithm trained using pelvic radiographs would help diagnose pre-collapse ONFH.

Performance Comparison of Different Neuroimaging Methods for Predicting Upper Limb Motor Outcomes in Patients after Stroke

Several neuroimaging methods have been proposed to assess the integrity of the corticospinal tract (CST) for predicting recovery of motor function after stroke, including conventional structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI). In this study, we aimed to compare the predicative performance of these methods using different neuroimaging modalities and optimize the prediction protocol for upper limb motor function after stroke in a clinical environment. We assessed 28 first-ever stroke patients with upper limb motor impairment. We used the upper extremity module of the Fugl-Meyer assessment (UE-FM) within 1 month of onset (baseline) and again 3 months poststroke. sMRI (T1- and T2-based) was used to measure CST-weighted lesion load (CST-wLL), and DTI was used to measure the fractional anisotropy asymmetry index (FAAI) and the ratio of fractional anisotropy (rFA). The CST-wLL within 1 month poststroke was closely correlated with upper limb motor outcomes and recovery potential. CST‐wLL ≥ 2.068 cc indicated serious CST damage and a poor outcome (100%). CST‐wLL < 1.799 cc was correlated with a considerable rate (>70%) of upper limb motor function recovery. CST-wLL showed a comparable area under the curve (AUC) to that of the CST-FAAI (p = 0.71). Inclusion of extra-CST-FAAI did not significantly increase the AUC (p = 0.58). Our findings suggest that sMRI-derived CST-wLL is a precise predictor of upper limb motor outcomes 3 months poststroke. We recommend this parameter as a predictive imaging biomarker for classifying patients' recovery prognosis in clinical practice. Conversely, including DTI appeared to induce no significant benefits.